Radar device with phase noise estimation

ABSTRACT

A method for estimating phase noise of an RF oscillator signal in a frequency-modulated continuous-wave (FMCW) radar system and related radar devices are provided. The method includes applying the RF oscillator signal to an artificial radar target composed of circuitry, which applies a delay and a gain to the RF oscillator signal, to generate an RF radar signal. Furthermore, the method includes down-converting the RF radar signal received from the artificial radar target from an RF frequency band to a base band, digitizing the down-converted RF radar signal to generate a digital radar signal, and calculating a decorrelated phase noise signal from the digital radar signal. A power spectral density of the decorrelated phase noise is then calculated from the decorrelated phase noise signal, and the power spectral density of the decorrelated phase noise is converted into a power spectral density of the phase noise of an RF oscillator signal.

FIELD

The present disclosure generally relates to the field of radar sensorsystems and devices, and signal processing employed in such systems anddevices. In particular, the invention relates to the estimation andcancellation of phase noise, which may be caused by undesired radarechoes from short range (SR) targets.

BACKGROUND

Radar systems are well-known in the art, and can generally be dividedinto pulse radar systems and continuous-wave (CW) systems. A pulse radarsystem measures a distance to an object (usually referred to as target)by transmitting a short radio-frequency (RF) pulse to an object, andmeasuring the time taken for the reflected pulse (i.e. the echo) to bereceived. As the velocity of the pulse is known (i.e. the speed oflight), it is straightforward to calculate the distance to an object.However, pulse radar systems are not suitable for use measuringdistances of a few 100 meters, in particular because the pulse lengthmust be reduced as the travel time (i.e. distance to the target)decreases. As the pulse length decreases, the energy contained in thepulse decreases, to the point where it becomes impossible to detect thereflected signal. Instead, continuous-wave radar systems are used formeasuring comparably short distances. In many applications, such as inautomotive applications, so-called frequency-modulated continuous-wave(FMCW) radar systems are used to detect targets in front of the radardevice and measure the distance to the target as well as their velocity.

Different from pulsed radar systems, in which isolation between thetransmit signal path and the receive signal path is not specificallyrelevant due to the pulsed operation of the transmitter, a phenomenonreferred to as leakage is an issue in FMCW radar systems. Leakagegenerally describes the problem that a small fraction of thefrequency-modulated transmit signal “leaks” into the receive signal pathof the radar transceiver without being back-scattered by a target. Ifthe cause of the leakage is in the RF frontend of the radar transceiver(i.e. imperfect isolation of the circulator, which separates receivesignal and transmit signal in a monostatic radar configuration) leakageis also referred to as crosstalk between the transmit signal path andthe receive signal path. When integrating the radar system in one singlemonolithic microwave integrated circuit (MMIC) crosstalk or so-calledon-chip leakage is usually an issue.

Another cause of leakage may be objects, which are very close to theradar antenna (such as, e.g., a fixture or a cover mounted a fewcentimeters in front of the radar antennas). Herein, reflections of thetransmitted radar signal at such objects (also referred to asshort-range targets) are referred to as short-range leakage, which is afraction of the transmit signal emanating from the transmit antenna andreflected back (back-scattered) to the receive antenna of the FMCW radarsystem at one or more short-range targets, which are very close to theradar antenna(s). It shall be understood that the transmit antenna andthe receive antenna are physically the same antenna in monostatic radarsystems. Herein, the mentioned reflections caused by short-range targetsare referred to as short-range leakage as their effect is similar to theeffect of on-chip leakage. However, known methods, which are suitablefor the cancellation of on-chip leakage or cross-talk are not suitablefor the cancellation of short-range leakage.

In radar systems the overall noise floor limits the sensitivity, withwhich radar targets can be detected, and thus also limits the accuracyof the distance measurement. Generally, this noise floor is dominated bythe additive noise of the transmission channel. However, in case ashort-range target reflects the transmitted radar signal with comparablyhigh amplitude (i.e. causes short-range leakage) the phase noise (PN) ofthe transmitted radar signal may dominate the noise floor. The phasenoise results in a deteriorated signal detection quality or even makesthe detection of radar targets with small radar cross sectionsimpossible. Thus, estimation of the phase noise may be of interest in aFMCW radar system.

SUMMARY

Exemplary embodiments disclosed herein relate to a radar device andrelated methods. As one exemplary embodiment, a method for estimatingphase noise of an RF oscillator signal in an FMCW radar system isdescribed. In the present example the method comprises applying the RFoscillator signal to an artificial radar target composed of circuitry,which applies a delay and a gain to the RF oscillator signal, togenerate a RF radar signal. The method further comprises down-convertingthe RF radar signal received from the artificial radar target from a RFfrequency band to a base band, digitizing the down-converted RF radarsignal to generate a digital radar signal, and calculating adecorrelated phase noise signal from the digital radar signal. A powerspectral density of the decorrelated phase noise is calculated from thedecorrelated phase noise signal, and the power spectral density of thedecorrelated phase noise is then converted into a power spectral densityof the phase noise of the RF oscillator signal.

In another embodiment the method comprises applying the RF oscillatorsignal to an artificial radar target composed of circuitry, whichapplies a delay and a gain to the RF oscillator signal, to generate a RFradar signal, down-converting the RF radar signal received from theartificial radar target from a RF frequency band to a base band,digitizing the down-converted RF radar signal to generate a digitalradar signal, and calculating a power spectral density of the digitalradar signal. A power spectral density of a deterministic summand of thedigital radar signal is calculated and, subsequently, a power spectraldensity of the phase noise of the RF oscillator signal is calculatedbased on the power spectral density of the digital radar signal and thepower spectral density of the deterministic summand.

Moreover, a radar device is described herein. In one exemplaryembodiment, the radar device includes a local oscillator generating a RFoscillator signal, which includes phase noise, and an artificial radartarget composed of circuitry, which applies a delay and a gain to the RFoscillator signal, to generate a RF radar signal. The radar devicefurther includes a first frequency conversion circuit configured todown-convert the RF radar signal received from the artificial radartarget from a RF frequency band to a base band and an analog-to digitalconversion unit configured to digitize the down-converted RF radarsignal to generate a digital radar signal. A signal processing unit ofthe radar device is configured to calculate a decorrelated phase noisesignal from the digital radar signal, to calculate a power spectraldensity of the decorrelated phase noise from the decorrelated phasenoise signal, and to calculate the power spectral density of thedecorrelated phase noise into a power spectral density of the phasenoise of an RF oscillator signal.

In another exemplary embodiment, the radar device includes a localoscillator generating a RF oscillator signal, which includes phasenoise, and an artificial radar target composed of circuitry, whichapplies a delay and a gain to the RF oscillator signal, to generate a RFradar signal. The radar device further includes a first frequencyconversion circuit configure to down-convert the RF radar signalreceived from the artificial radar target from a RF frequency band to abase band, and an analog-to digital conversion unit configured todigitize the down-converted RF radar signal to generate a digital radarsignal. A signal processing unit of the radar device is configured tocalculate a power spectral density of the digital radar signal, tocalculate a power spectral density of a deterministic summand of thedigital radar signal, and to calculate a power spectral density of thephase noise of the RF oscillator signal based on the power spectraldensity of the digital radar signal and the power spectral density of adeterministic summand.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention can be better understood with reference to the followingdrawings and descriptions. The components in the figures are notnecessarily to scale; in-stead emphasis is placed upon illustrating theprinciples of the invention. More-over, in the figures, like referencenumerals designate corresponding parts. In the drawings:

FIG. 1 is a schematic diagram illustrating the operating principle of aFMCW radar sensor with a single radar target in a transmission channelaccording to one or more embodiments;

FIG. 2 illustrates the waveform of the transmitted and reflected radarsignals in the radar sensor of FIG. 1 according to one or moreembodiments;

FIG. 3 is a block diagram illustrating the function of the radar sensorof FIG. 1 according to one or more embodiments;

FIG. 4 is a simplified block diagram representing a function of a FMCWradar sensor according to one or more embodiments;

FIG. 5 is a schematic diagram illustrating a cause and origination ofleakage by reflection at a short range target according to one or moreembodiments;

FIG. 6 is a block diagram illustrating a radar sensor with noisecancellation in accordance with one or more embodiments;

FIG. 7 is a diagram illustrating a decorrelated phase noise fordifferent delay times according to one or more embodiments;

FIG. 8 is a diagram illustrating a cross-correlation coefficient betweena decorrelated phase noise included in short-range leakage and adecorrelated phase noise included in a signal obtained from anartificial on-chip target according to one or more embodiments;

FIG. 9 is a flow chart illustrating noise cancellation in accordancewith one or more embodiments;

FIG. 10 is a diagram illustrating one example of the calculation of thephase noise of a local oscillator according to one or more embodiment;and

FIG. 11 is a diagram illustrating another example of the calculation ofthe phase noise of a local oscillator according to one or moreembodiments.

DETAILED DESCRIPTION

FIG. 1 illustrates a conventional frequency-modulated continuous-wave(FMCW) radar system 100. In the present example, separate transmit (TX)and receive (RX) antennas 101 and 102, respectively, are used (bistaticor pseudo-monostatic radar configuration). However, it shall beunderstood that a single antenna can be used so that the receive antennaand the transmit antenna are physically the same (monostatic radarconfiguration). The transmit antenna continuously radiates a sinusoidalRF signal s_(RF)(t), which is frequency-modulated, for example, by asaw-tooth signal (periodic linear ramp signal, see also FIG. 2). Thetransmitted signal s_(RF)(t) is back-scattered at a target T₁, which islocated within the measurement range of the radar system and received byreceive antenna 102. The received signal is denoted as y_(RF)(t). In theradar device 100, the received signal y_(RF)(t) is demodulated by mixingthe signal y_(RF)(t) with a copy of the transmit signal s_(RF)(t) toeffect a down-conversion of the RF signal y_(RF)(t) into the base band.This down-conversion is illustrated in FIG. 2. The received RF signaly_(RF)(t) lags behind the transmit signal s_(RF)(t) due to the timetaken for the signal to travel to and from the target T₁. As aconsequence, there is a constant frequency difference between thereceived RF signal y_(RF)(t) and the reference signal (i.e. the copy ofthe transmit signal s_(RF)(t)). When the two signals s_(RF)(t) andy_(RF)(t) are mixed (i.e. demodulated), a demodulated signal y(t) ofconstant frequency (in case of a linear frequency modulation) isobtained (also referred to as beat frequency). The beat frequency of thereceived and demodulated signal y(t) can be determined (e.g. usingFourier analysis) and used to calculate the distance between the radardevice 100 and the target T₁.

The radar device 100 may include or be implemented in a monolithicmicrowave integrated circuit (MMIC), which includes circuitry forproviding the core functions needed for distance and/or velocitymeasurement in one chip (also referred to as “single chip radar”). Thusthe chip may include, inter alia, RF oscillators, amplifiers, mixers,filters, analog-to-digital converters, and digital signal processors.FIG. 3 illustrates the transmit path and the receive path of a radartransceiver, which may be used for distance measurement in a radardevice 100 shown in FIG. 1. Accordingly, the RF transceiver 1 includes amixer 110, which is supplied with radar signal y_(RF)(t) and with RFoscillator signal s_(RF)(t) used to down-convert the radar signaly_(RF)(t) into the base band. The radar signal y_(RF)(t) (i.e. a backscattered portion of the transmit signal s_(RF)(t)) is received byreceive antenna 102 and may be pre-amplified (see RF amplifier 105, e.g.a low noise amplifier LNA) before being supplied to the mixer 110. Inthe present example, the RF oscillator signal s_(RF)(t) is generated bya local oscillator (LO) 103, which may include a voltage controlledoscillator (VCO) coupled in a phase locked loop (PLL). However, the RFoscillator signal s_(RF)(t) may be provided by other circuitry dependenton the actual application. When used in a radar distance measurementdevice, the RF oscillator signal s_(RF)(t) may be in the range betweenapproximately 24 GHz and 81 GHz (approximately 77 GHz in the presentexample). However, higher or lower frequencies may also be applicable.The RF oscillator signal s_(RF)(t) is also supplied to transmit antenna101 (e.g. via power amplifier 104) and radiated towards the radar target(see also FIG. 1).

As mentioned, the mixer 110 down-converts the radar signal (amplifiedantenna signal A y_(RF)(t), amplification factor A) into the base band.The respective base band signal (mixer output signal) is denoted byy(t). The base band signal y(t) is then subject to analog filtering(filter 115) to suppress undesired sidebands or image frequencies, whichmay be a result of the mixing operation. The filter 115 may be alow-pass filter or a band-pass filter. The filtered base band signal(filter output signal) is denoted by y′(t). Receivers (e.g. the receiverportions of transceivers) which make use of a mixer to down-convert thereceived RF signal into the base band are as such known as heterodynereceivers and thus not further discussed in more detail. The filteredbase band signal y′(t) is then sampled (temporal discretization) andconverted to a digital signal y[n] (analog-to-digital converter (ADC)120), which is then further processed in the digital domain usingdigital signal processing (n being the time index). The digital signalprocessing may be performed in a digital signal processing unit 125,which may include, e.g., a digital signal processor (DSP) executingappropriate software instructions.

FIG. 3 illustrates the receive path of a radar transceiver 100 of aso-called bistatic or pseudo-monostatic radar system, in which thereceiver may be separate from the transmitter (as receiver andtransmitter use separate antennas). In the present example, thetransmitter and the receiver portion of the radar transceiver are,however, integrated in one MMIC. In a monostatic radar system, the sameantenna is used to transmit and receive RF radar signals. In such cases,the radar transceiver additionally includes a directional coupler or acirculator (not shown) coupled between the mixer for separating the RFtransmit signal s_(RF)(t) from the received signal y_(RF)(t).

The transmission channel 200 represents the signal path from thetransmit antenna 101 to the target and back to the receive antenna 102.While passing through the transmission channel the radar signalss_(RF)(t) (transmitted signal) and y_(RF)(t) (back-scattered signal) aresubject to additive noise w(t), which is usually modelled as additivewhite Gaussian noise (AWGN). FIG. 4 is a simplified block diagramillustrating the analog frontend of the radar transceiver shown in FIG.3. To allow a simple and clear illustration, antennas and amplifiershave been omitted. Accordingly, the RF transmit signal s_(RF)(t), whichmay be generated by local oscillator 103, is sent through transmissionchannel 200 and finally arrives (as received radar signal y_(RF)(t)) atthe RF input of mixer 110, which is configured to down-convert the radarsignal y_(RF)(t) into the base band. The resulting base band signal y(t)(beat signal) is low-pass filtered (low-pass filter 115), and thefiltered base band signal y′(t) is then digitized usinganalog-to-digital converter 120. Band-pass filtering may also beapplicable instead of low-pass filtering. The digitized base band signaly[n] is then further processed digitally to estimate the distancebetween the transceiver 100′ and the target. As mentioned additive whiteGaussian noise is added to the radar signal while passing through thetransmission channel 200.

FIG. 5 is basically the same illustration as shown in FIG. 1 but with anadditional object Ts located in the transmission channel 200 comparablyclose to the antennas (e.g., a fixture or a cover mounted in front ofthe radar antennas). Such objects are herein referred to as short-rangetargets. A short-range targets is usually located a few centimeters(e.g. less than 50 cm) in front of the radar device (which is less thanthe lower margin of the measurement range of the radar system) andreflects a portion of the transmit signal s_(RF)(t) back to the receiveantenna 102. As mentioned above, such reflections at short-range targetsgive rise to a phenomenon referred to as short-range leakage. In theexample of FIG. 5, the transmitted RF signal s_(RF)(t) is back-scatteredat target T₁ (which is within the normal measurement range of the radartransceiver) as well as reflected at the short-range target Ts. Thesignal back-scattered from target T₁ is denoted as y_(RF,1)(t) and thesignal reflected at the short-range target Ts is denoted as y_(RF,S)(t).Both signals y_(RF,S)(t) and y_(RF,S)(t) superpose and the resulting sumsignal y_(RF)(t) is received by the antenna 102. Considering the factthat the received signal power decreases with the fourth power of thedistance, the signal amplitude of the radar signal y_(RF,S)(t) due toshort-range leakage is significant. Furthermore, the phase noise of thetransmitted radar signal s_(RF)(t) is the dominant cause of noise in thereceived radar signal y_(RF)(t) as a result of the short-range leakage.

FIG. 6 is a block diagram of a radar transceiver in accordance with oneexemplary embodiment, which is configured to cancel short-range leakageand thus the mentioned phase noise from the received radar signal usingdigital signal processing in the base band and an artificial radartarget 300 (further referred to as on-chip target or OCT). Again,antennas and amplifiers have been omitted in the illustration for thesake of simplicity and clarity. The transmit signal s_(RF)(t) is afrequency-modulated continuous-wave (FMCW) signal (chirp-signal), alsoreferred to as chirp signal. Accordingly, the signal s_(R)F (t) can bewritten as:s _(RF)(t)=cos(2πf ₀ t+πkt ²+φ(t)+Φ),  (1)wherein f₀ is the start frequency of the chirp signal, k (k=B/T) denotesthe slope of the chirp with bandwidth B and duration T, Φ is a constantphase offset and φ(t) is the introduced phase noise (PN) due toimperfections of the local oscillator (see FIG. 3).

The transmission channel 200 (see FIGS. 5 and 6) comprises of two typesof signal reflections. Firstly, reflections (back-scattering) at targetsT_(i), whose distances from the radar transceiver are to be measured.These targets T_(i) are modeled by a delay τ_(Ti) and gain A_(Ti),wherein i=1, . . . , N_(T), and N_(T) denotes the number of targetsT_(i) (not including the short-range target). Secondly, the reflectionat a short-range target T_(S), which represents the undesired neartarget causing reflections (short-range leakage) which are to becancelled. Analogously to a normal target the short-range target may bemodeled by a delay τ_(S) and gain A_(S). In practice, the gain A_(S)will be significantly larger than any of the gains A_(Ti). This model ofthe transmission channel 200 is depicted in the upper signal path of theblock diagram of FIG. 6. At the receiver side, additive white Gaussiannoise (AWGN) w(t) is added before down-conversion to the base band isdone. Consequently, the received RF radar signal y_(RF)(t) may bewritten as:y _(RF)(t)=A _(S) ·S _(RF)(t−τ _(S))+Σ_(i=1) ^(N) ^(T) A _(Ti) ·s_(RF)(t−τ _(Ti))+w(t),  (2)wherein the first summand represents the signal component due to theshort-range leakage, the second summand represents the signal componentsdue to reflections at the “normal” radar target(s) and the last summandrepresents AWGN. The delays τ_(S) and τ_(Ti) are also referred to asround trip delay times (RTDT) associated with the short-range targetT_(S) and the targets T_(i), respectively. It should be noted that, inthe present disclosure, the previously mentioned on-chip leakage is notconsidered as several concepts for cancelling on-chip leakage exist.

As can be seen from FIG. 6, the received radar signal is subject to adown-conversion using the mixer 110 and a subsequent band-pass orlow-pass filtering using the filter 115, which has a filter impulseresponse h_(F)(t). As in the previous illustrations, the down-convertedand filtered signal is denoted as y′(t), which can be modelled asfollows (assuming Φ=0 for the sake of simplicity):

$\begin{matrix}\begin{matrix}{{y^{\prime}(t)} = {{\left( {{s_{RF}(t)} \cdot {y_{RF}(t)}} \right)*{h_{F}(t)}} =}} \\{= {{\frac{A_{S}}{2} \cdot {\cos\left( {{2\pi\; f_{BS}t} + \Phi_{S} + {\varphi(t)} - {\varphi\left( {t - \tau_{S}} \right)}} \right)}} +}} \\{{\sum\limits_{i = 1}^{N_{T}}{\frac{A_{Ti}}{2} \cdot {\cos\left( {{2\pi\; f_{{BT}_{i}}t} + \Phi_{T_{i}} + {\varphi(t)} - {\varphi\left( {t - \tau_{T_{i}}} \right)}} \right)}}} + {{w(t)}.}}\end{matrix} & (3)\end{matrix}$The beat frequencies resulting from the short-range leakage and thereflections at the normal targets are denoted as f_(BS) and f_(BT) _(i)(target T_(i)), respectively, and can be represented by the followingequations:f _(BS) =kτ _(S), and f _(BT) _(i) =kτ _(T) _(i) .  (4)Furthermore, the constant phase Φ_(S) and Φ_(T) _(i) can be computed as:Φ_(S)=2πf ₀τ_(S) −kπτ _(S) ², and Φ_(T) _(i) =2πf ₀τ_(T) _(i) −kπτ _(T)_(i) ².  (5)The beat frequencies (equations 4) and constant phases (equations 5)depend only on given system parameters (such as the start frequency f₀of the chirp as well as its bandwidth and duration as represented by thevariable k=B/T) and the RTDTs τ_(S) and τ_(Ti) associated with theshort-range leakage and the radar targets T_(i) to be detected,respectively. It follows from equations 3, 4 and 5 that the signalcomponent of y′(t), which results from the short-range leakage (i.e. thefirst summand in equation 3), is zero when the RTDT τ_(S) is zero(τ_(S)=0). Even the term φ(t)−φ(t−τ_(S)) becomes zero when the delaytime τ_(S) is zero. With increasing values of the RTDT τ_(S) (i.e. withincreasing distance of the short-range target) the correlation of thephase noise components φ(t) and φ(t−τ_(S)) decreases. This effect iscalled range correlation effect and the phase difference φ(t)−φ(t−τ_(S))is referred to as decorrelated phase noise DPN. It is noted that DPN isusually not an issue in the context of on-chip leakage as the associateddelay is negligibly small.

In the following, the first summand of equation 3, i.e. the short-rangeleakage signal

$\begin{matrix}{{y_{S}^{\prime}(t)} = {\frac{A_{S}}{2} \cdot {\cos\left( {{2\pi\; f_{BS}t} + \Phi_{S} + {\varphi(t)} - {\varphi\left( {t - \tau_{S}} \right)}} \right)}}} & (6)\end{matrix}$is analyzed in more detail (see FIG. 6). In equation 6, the gain A_(S)/2is primarily determined by the radar cross section (RCS) of theshort-range target. Generally, the RCS may depend on the shape and thematerial of the short-range target. The beat frequency Gs (see equation4) depends on the RTDT τ_(S) associated with the short-range target. TheRTDT τ_(S) depends on the distance d_(S) between the radar device andthe short-range target. Accordingly, the distance d_(S) can becalculated as d_(S)=c·τ_(S)/2, wherein c denotes the speed of light. Inequation 6, the DPN φ(t)−φ(t−τ_(S)) represents noise in addition to thementioned AWGN. To analyze how the DPN affects the spectrum of thereceived radar signal, the power spectrum S_(ΔφS,ΔφS) (f) of the DPN iscalculated:S _(ΔφS,ΔφS)(f)=S _(φ,φ)(f)·2(1−cos(2πτ_(S) f)),  (7)wherein S_(φ,φ)(f) is the power spectrum of the phase noise signal φ(t)included in the RF transmit signal s_(RF)(t). Further analysis of arealistic example (τ_(S)=800 ps, d_(S)≈12 cm) shows that, forfrequencies higher than 100 kHz, the noise level of the DPN is −140dBm/Hz, assuming a transmit power of 10 dBm and an AWGN noise floor of−140 dBm/Hz. The presence of DPN entails an increase of the noise floorand results in a 10 dB reduction of sensitivity for the detection ofradar targets. As a result, the total noise floor increases, which isequivalent to a loss of sensitivity of 10 dB for the detection of radartargets.

To at least reduce the effect of the DPN due to (unavoidable)short-range targets an (artificial) on-chip target (OCT) is included inthe radar device and incorporated in the signal processing chain asillustrated in FIG. 6. The OCT is used to obtain an estimation of theDPN and to (at least partially) cancel the DPN from the received radarsignal in the base band. As can be seen from FIG. 6, the RF transmitsignal s_(RF)(t) is (in addition to being radiated to the radar channel200) supplied to OCT 300 that is basically composed of a gain A_(O)(A_(O)<1) and a delay τ_(O), which can be seen as an on-chip RTDT. TheRF signal received from OCT 300 is denoted as y_(RF,O)(t). This signaly_(RF,O)(t) is down-converted into the base band (mixer 110′) andband-pass filtered (filter 115′) in the same manner as the RF signaly_(RF)(t) received from the radar channel 200. The down-converted signalreceived from OCT 300 is denoted as y_(O)(t) and the respectiveband-pass (or low-pass) filtered signal is denoted as y_(O)′(t). Both,the filtered base band signal y′(t) received from radar channel 200 andthe filtered base band signal y_(O)′(t) received from OCT 300 aredigitized using analog-to-digital converters 120 and 120′, respectively,for further digital signal processing. In another embodiment a singleanalog-to-digital converter and a multiplexer may be used to provide thesame function. The respective digital signals are denoted as y[n] andy_(O)[n].

Theoretically, it would be desirable that the delay τ_(O) of OCT 300equals the RTDT τ_(S) of the short-range target Ts present in radarchannel 200. In realistic examples the RTDT τ_(S) of the short-rangetarget T_(S) is in the range of a few hundreds of picoseconds up to afew nanoseconds, whereas the delay τ_(O) of an on-chip target ispractically limited to a few picoseconds when implementing the radardevice on a single MMIC. In a single-chip radar higher values of delayτ_(O) (which would be needed in case of τ_(O)=τ_(S)) would result in anundesired (or even unrealistic) increase in chip area and powerconsumption and are thus only economically feasible when using discretecircuit components. Therefore, the delay τ_(O) of OCT 300 is limited tovalues that are significantly lower than the RTDT τ_(S) of anypractically relevant short-range target T_(S).

Further analysis of the properties of the cross-correlation coefficientof the decorrelated phase noise (DPN) signalsΔφ_(S)(t)=φ(t)−φ(t−τ _(S)),  (8)i.e. the DPN included in the RF signal received from the short-rangetarget Ts (see FIGS. 5 and 6), andΔφ_(O)(t)=φ(t)−φ(t−τ _(O))  (9)i.e. the DPN included in the RF signal received from OCT 300, shows thatthe cross-correlation coefficient

$\begin{matrix}{{\rho_{{\Delta\;\varphi_{O}},{\Delta\varphi}_{S}}(l)} = \frac{E\left\{ {\Delta\;{\varphi_{O}(t)}{{\Delta\varphi}_{S}\left( {t - l} \right)}} \right\}}{\sqrt{\sigma_{\Delta\;\varphi_{O}}^{2}}\sqrt{\sigma_{\Delta\;\varphi_{S}}^{2}}}} & (10)\end{matrix}$is very similar for different values of OCT delay τ_(O) (the operator Edenoting the expected value and σ_(Δφ) _(O) ² and σ_(Δφ) _(S) ² are therespective variances). Note that the DPN terms are assumed to have amean value of zero. For an OCT delay τ_(O) equal to the RTDT r_(S), thecross-correlation coefficient assumes a maximum for a time lag l of zero(l=0). For smaller values of τ_(O) (i.e. τ_(O)<τ_(S)) thecross-correlation coefficient is scaled and shifted as compared to thecase when τ_(O)=τ_(S). This result is illustrated in the diagrams ofFIGS. 7 and 8.

FIG. 7 illustrates exemplary realizations of a DPN signalΔφ(t)=φ(t)−φ(t−τ) for different delay times τ. The DPN signals Δφ(t)shown in FIG. 7 (for τ=40 ps, τ=160 ps, τ=400 ps, and τ=800 ps) havebeen obtained by simulating the phase noise φ(t) using a stochasticmodel, which models the phase noise of the local oscillator (see FIG. 3,LO 103). It can be seen from FIG. 7 that the waveforms of the resultingDPN signals are very similar, even when the delay time r is different.In this context similar means that one waveform (e.g. for τ=40 ps) canbe transformed into any other waveform (e.g. the waveform for τ=800 ps)by applying a gain and a time-shift (equivalent to phase-shift). Thisfact can also be observed in the cross-correlation coefficient shown inFIG. 8. Equation 10 has been estimated with a discrete-time simulation,wherein the expected value (operator E) has been approximated over arepresentative length of the random signals (obtained using thementioned stochastic model) representing phase noise signal φ(t).

As the DPN Δφ_(O)(t) included in the down-converted RF signal

$\begin{matrix}{{{y_{O}(t)} = {\frac{A_{O}}{2} \cdot {\cos\left( {{2\pi\; f_{BO}t} + \Phi_{O} + {\varphi(t)} - {\varphi\left( {t - \tau_{O}} \right)}} \right)}}},} & (11)\end{matrix}$which is received from OCT 300, and the DPN Δφ_(S)(t) included in thebaseband signal y_(S)(t) which is received from the short-range target(see equation 6), are highly correlated, the DPN included in thebaseband signal y_(O)(t) obtained from OCT 300 can be used to estimatethe DPN caused by the short-range leakage. In equation 11, f_(BO)denotes the beat frequency caused by OCT 300 and is calculatedanalogously to f_(BS) (see equation 4). Also the constant phase Φ_(O) iscomputed in an analogous manner as constant phase Φ_(S) (see equations 5and 14). In a practical example, the RTDT τ_(S) associated with theshort-range target Ts is approximately 800 ps (corresponds to d_(S)=12cm), whereas the OCT delay time τ_(O) is only 40 ps. Therewith, the beatfrequency f_(BS) is 20 times higher than beat frequency f_(BO).

As can be seen from FIG. 6, the sampling clock signal, which triggersthe sampling of the upper signal path (i.e. the sampling of signal y′(t)received from channel 200), is delayed by a time offset ΔT_(A). Thistime offset of the sampling clock signal may be chosen equal to the timelag l, at which the cross-correlation coefficient (see equation 10 andFIG. 8) has its maximum for a specific RTDT τ_(O), wherein τ_(O)<τ_(S).Further analysis of the cross-correlation coefficient shows that theoptimum sampling time offset ΔT_(A) is equal to half of the differenceτ_(S)−τ_(O), that is

$\begin{matrix}{{\Delta\; T_{A}} = {\frac{\tau_{S} - \tau_{O}}{2}.}} & (12)\end{matrix}$Using the mentioned sampling time offset for maximization of thecorrelation coefficient results in a high correlation coefficient ρ_(Δφ)_(O) _(,Δφ) _(S) (0) of, for example, 0.9 for τ_(S)=800 ps and τ_(O)=40ps (see diagram of FIG. 8).

As the DPN signals included in the discrete time signals y[n] andy_(O)[n] (provided by analog-to-digital converters 120 and 120′,respectively) are highly correlated (particularly when using thementioned sampling time offset), an estimation of the discrete-time DPNsignal Δφ_(O)[n] may be calculated from the down-converted signaly_(O)[n] obtained from OCT 300. This estimation and the subsequentcalculation of a corresponding cancellation signal is performed by thefunction block 130 labelled LC (leakage cancellation). Therefore, the LCfunction block basically provides the two functions of estimating theDPN from signal y_(O)[n] and generating a cancellation signal ŷ_(S) [n]to be subtracted from the down-converted and digitized radar signal y[n]in order to eliminate the short-range leakage (see also equation 6)included in the radar signal y[n].

The discrete-time version of equation 11 is

$\begin{matrix}{{y_{O}\lbrack n\rbrack} = {\frac{A_{O}}{2} \cdot {\cos\left( {{2\pi\; f_{BO}{nT}_{A}} + \Phi_{O} + {\Delta\;{\varphi_{O}\lbrack n\rbrack}}} \right)}}} & (13) \\{with} & \; \\{{f_{BO} = {k\tau}_{O}},{{{and}\mspace{14mu}\Phi_{O}} = {{2\pi\; f_{0}\tau_{O}} - {k\;{\pi\tau}_{O}^{2}}}}} & (14)\end{matrix}$wherein f_(A) is the sampling rate determined by the period T_(A) of thesampling clock signal (f_(A)=T_(A) ⁻¹). Applying the trigonometricidentitycos(a+b)=cos(a)cos(b)+sin(a)sin(b)  (15)and the approximations (since Δφ_(O)[n] is sufficiently small)cos(Δφ_(O)[n])≈1 and  (16)sin(Δφ_(O)[n])≈Δφ_(O)[n]  (17)to equation 13 simplifies it to

$\begin{matrix}{{y_{O}\lbrack n\rbrack} \approx {{\frac{A_{O}}{2} \cdot {\cos\left( {{2\pi\; f_{BO}{nT}_{A}} + \Phi_{O}} \right)}} - {\frac{A_{O}}{2} \cdot {\sin\left( {{2\pi\; f_{BO}{nT}_{A}} + \Phi_{O}} \right)} \cdot {{\Delta\varphi}_{O}\lbrack n\rbrack}}}} & (18)\end{matrix}$

As the gain A_(O) and the beat frequency f_(BO) are a-priori knownsystem parameters of the radar system the DPN Δφ_(O)[n] can beapproximated based on the down-converted signal y_(O) [n], which isreceived from the OCT, in accordance with the following equation:

$\begin{matrix}{{{\Delta\varphi}_{O}\lbrack n\rbrack} \approx \frac{{\frac{A_{O}}{2} \cdot {\cos\left( {{2\pi\; f_{BO}{nT}_{A}} + \Phi_{O}} \right)}} - {y_{O}\lbrack n\rbrack}}{\frac{A_{O}}{2} \cdot {\sin\left( {{2\pi\; f_{BO}{nT}_{A}} + \Phi_{O}} \right)}}} & (19)\end{matrix}$

Beat frequency f_(BO) and phase Φ_(O) may be measured after productionof the radar device as a part of a system test and calibrationprocedure. These parameters can be computed in the same manner as forthe short-range leakage signal y_(S)[n] (see equations 4 and 5 andequation 14). In order to account for parameter variations of OCT 300(e.g. due to temperature changes) beat frequency f_(BO) and phase Φ_(O)may be estimated repeatedly and updated regularly.

As the DPN signals Δφ_(O)[n] and Δφ_(S)[n] are highly correlated, theshort-range leakage signal (cf. equation 6)

$\begin{matrix}{{y_{S}\lbrack n\rbrack} = {\frac{A_{S}}{2} \cdot {\cos\left( {{2\pi\; f_{BS}{nT}_{A}} + \Phi_{S} + {{\Delta\varphi}_{S}\lbrack n\rbrack}} \right)}}} & (20)\end{matrix}$

can be approximated as

$\begin{matrix}{{{{\hat{y}}_{S}\lbrack n\rbrack} = {\frac{{\hat{A}}_{S}}{2} \cdot {\cos\left( {{2\pi\;{\hat{f}}_{BS}{nT}_{A}} + {\hat{\Phi}}_{S} + {\alpha_{L} \cdot {{\Delta\varphi}_{O}\lbrack n\rbrack}}} \right)}}},} & (21)\end{matrix}$

where α_(L) is referred to as DPN gain. Gain α_(L) can be determinedwith the help of the auto-covariance functionc _(Δφ) _(OL) _(,Δφ) _(OL) (l)=E{Δφ _(OL)(t)Δφ_(OL)(t−l)},  (22)

where Δφ_(OL)(t)=Δφ_(O)(t)*h_(L)(t), i.e. the convolution with theimpulse response of the lowpass filter 115′, and the cross-covariancefunctionc _(Δφ) _(OL) _(,Δφ) _(SL) (l)=E{Δφ _(OL)(t)Δφ_(SL)(t−l)}  (23)with Δφ_(SL)(t)=Δφ_(S)(t)*h_(L)(t). The DPN gain α_(L) can then bedetermined as

$\begin{matrix}{\alpha_{L} = {\frac{c_{{\Delta\varphi}_{OL},{\Delta\varphi}_{SL}}\left( {{- \Delta}\; T_{A}} \right)}{c_{{\Delta\varphi}_{OL},{\Delta\varphi}_{OL}}(0)}.}} & (24)\end{matrix}$Note that the numerator equals equation 23 (resulting in α_(L)=1) whenτ_(O)=τ_(S) (see also FIG. 8, in which the cross-correlation coefficienthas a maximum of 1 for τ_(O)=τ_(S) and maxima lower than 1 forτ_(O)<τ_(S)). Therewith, α_(L) is a measure of how much the DPN of theOCT needs to be amplified such that it approximates the DPN of the SRleakage. For example, with a typical phase noise power spectrum,τ_(S)=800 ps and τ_(O)=40 ps results in a DPN gain of α_(L)=19.8. Theparameters {circumflex over (Φ)}_(S) and Â_(S) are specific to the radarsystem (i.e. dependent on the short-range target) and can be calculatedor determined by calibration.

The estimated short-range leakage signal ŷ_(S)[n] is generated by the LCfunction block 130 illustrated in FIG. 6. The actual noise cancellationis accomplished by subtracting the estimated short-range leakage signalŷ_(S)[n] from the signal y[n] received from the radar channel. The DPNcompensated signal is denoted as z[n] and is calculated as:z[n]=y[n]−ŷ _(S)[n].  (25)The cancellation method is summarized in the flow-chart of FIG. 9. Ascompared to a known radar system the RF transmit signal s_(RF)(t) istransmitted to an on-chip target (OCT) 300 (see step 701). The signaly_(RF,O)(t) received from OCT 300 down-converted to the base band (baseband signal y_(O)(t), step 702) and digitized (digital base band signaly_(O)[n], step 703). The decorrelated phase noise (DPN) signal Δφ_(O)[n]is estimated from digitized signal y_(O)(t), and a correspondingcancellation signal ŷ_(S) [n] is generated based on the estimated DPNsignal Δφ_(O)[n] (step 704). Finally, the cancellation signal issubtracted from the (down-converted and digitized) radar echo signaly[n] in order to compensate for the short-range leakage includedtherein.

In some radar systems it may be of interest to measure the phase noise(PN) φ(t) of the local oscillator 103 (see FIG. 1 and equation 1). Asshown below, the power spectrum of the phase noise φ(t) can be derivedfrom the DPN Δφ_(O)[n] (see equation 19). In equation 1, the amplitude Aof the oscillator signal has been assumed to be one (A=1). For anarbitrary signal amplitude A of the oscillator signal s_(RF)(t) equation19 can be written as:

$\begin{matrix}{{\Delta\;{\varphi_{O}\lbrack n\rbrack}} \approx {\frac{{\frac{A_{O}}{2} \cdot {\cos\left( {{2\pi\; f_{BO}{nT}_{A}} + \Phi_{O}} \right)}} - \frac{y_{O}\lbrack n\rbrack}{A^{2}}}{\frac{A_{O}}{2} \cdot {\sin\left( {{2\pi\; f_{BO}{nT}_{A}} + \Phi_{O}} \right)}}.}} & (26)\end{matrix}$Accordingly, the power spectral density (PSD) can be estimated from thetime domain DPN signal Δφ_(O)[n], which is extracted from the signaly_(O)[n] received from the OCT 300. For an arbitrary signal amplitude Aequation 13 can be written as:

$\begin{matrix}{{y_{O}\lbrack n\rbrack} = {\frac{A^{2}A_{O}}{2} \cdot {{\cos\left( {{2\pi\; f_{BO}{nT}_{A}} + \Phi_{O} + {{\Delta\varphi}_{O}\lbrack n\rbrack}} \right)}.}}} & (27)\end{matrix}$It should be noted that this signal is readily available in an FMCWradar transceiver, which implements the short-range leakage cancelationconcept as shown, for example, in FIG. 6. In order to determine the PSDof the phase noise (PN) φ(t) the spectral properties of the DPN areinvestigated more closely.

The auto-covariance function of the DPN is:c _(Δφ) _(O) _(,Δφ) _(O) (u)=E{Δφ _(O)(t)Δφ_(O)(t+u)}  (28)which can be expanded to:c _(Δφ) _(O) _(,Δφ) _(O) (u)=E{φ(t)φ(t+u)}−E{φ(t)φ(t+u−τ _(O))}−E{φ(t−τ_(O))φ(t+u)}+E{φ(t−τ _(O))φ(t+u−τ _(O))}.  (29)The term E{φ(t)φ(t+u)} is the auto-covariance c_(φ,φ)(u) of the phasenoise φ(t) and thus equation 29 can be written as:c _(Δφ) _(O) _(,Δφ) _(O) (u)=2c _(φ,φ)(u)−c _(φ,φ)(u−τ _(O))−c_(φ,φ)(u+τ _(O)).  (30)Finally, the PSD S_(Δφ) _(O) _(,Δφ) _(O) (f) of the DPN can becalculated from equation 30 using the Wiener-Khinchine theorem asfollows:

$\begin{matrix}\begin{matrix}{{S_{{\Delta\varphi}_{O},{\Delta\varphi}_{O}}(f)} = {{\left( {c_{{\Delta\varphi}_{O},{\Delta\varphi}_{O}}(u)} \right)} =}} \\{= {{{2 \cdot {S_{\varphi,\varphi}(f)}} - {{S_{\varphi,\varphi}(f)} \cdot \left( {e^{{j \cdot 2}\pi\; f\;\tau_{O}} + e^{{{- j} \cdot 2}\pi\; f\;\tau_{O}}} \right)}} =}} \\{{= {{2 \cdot {S_{\varphi,\varphi}(f)}} - {2\;{{S_{\varphi,\varphi}(f)} \cdot \left( {1 - {\cos\left( {2\pi\; f\;\tau_{O}} \right)}} \right)}}}},}\end{matrix} & (31)\end{matrix}$wherein

denotes the Fourier transform. Rearranging equation 31 results in

$\begin{matrix}{{{S_{\varphi,\varphi}(f)} = \frac{S_{{\Delta\varphi}_{O},{\Delta\varphi}_{O}}(f)}{2\left( {1 - {\cos\left( {2\pi\; f\;\tau_{O}} \right)}} \right)}},} & (32)\end{matrix}$wherein S_(φ,φ)(f) is the desired PSD of the phase noise φ(t).

The PSD S_(Δφ) _(O) _(,Δφ) _(O) (f) of the DPN can be estimated from theDPN discrete time domain signal Δφ_(O)[n] as given in equation 26. Toobtain the desired PSD of the phase noise φ(t) equation 8 is evaluated.Therefore only the known system parameter τ_(O) (the delay time of theon-chip target 300) as well as A and A_(O) are needed. It is noted thatthe resulting PSD S_(φ,φ)(f) of the phase noise φ(t) is evaluated overthe whole chirp bandwidth B rather than at a fixed frequency. Thepresent approach is summarized by the diagram of FIG. 10, which shows apart of the receive signal path of the (down-converted) radar signaly_(O)(t) received from OCT 300 (see also FIG. 6). As already discussedthe signal y_(O)(t) is filtered, e.g. by band-pass 115′, and thefiltered signal y_(O)′(t) is digitized, e.g. by ADC 120′ to obtain thedigital signal y_(O)[n]. The digital signal y_(O)[n] can be used fornoise cancellation as explained above with reference to FIGS. 6 to 9. Inthe present example, the DPN Δφ_(O) [n] is calculated (approximated)from the digital signal y_(O)[n] (function block 401), e.g., inaccordance with equation 26. For this, no additional calculations arerequired if the DPN is calculated in the leakage calculation block 103′.From the DPN signal Δφ_(O)[n] the PSD S_(Δφ) _(O) _(,Δφ) _(O) (f) iscalculated by known algorithms (e.g. Welch's method, function block402), and finally the desired PSD S_(φ,φ)(f) can be calculated fromS_(Δφ) _(O) _(,Δφ) _(O) (f) using equation 32 (function block 403).

Alternatively, the desired PSD of the phase noise φ(t) can be deriveddirectly from the digital signal y_(O)[n] without the need to calculatethe DPN as in the previous example. Therefore the digital signaly_(O)[n] is approximated as shown before in equation 18, which resultsin:

$\begin{matrix}\begin{matrix}{{y_{O}\lbrack n\rbrack} \approx {{\frac{A^{2}A_{O}}{2} \cdot {\cos\left( {{2\pi\; f_{BO}{nT}_{A}} + \Phi_{O}} \right)}} -}} \\{\frac{a^{2}A_{O}}{2} \cdot {\sin\left( {{2\pi\; f_{BO}{nT}_{A}} + \Phi_{O}} \right)} \cdot {{\Delta\varphi}_{O}\lbrack n\rbrack}} \\{= {{y_{O\; 1}\lbrack n\rbrack} - {{y_{O\; 2}\lbrack n\rbrack}.}}}\end{matrix} & (33)\end{matrix}$The generally time-dependent PSD of y_(O)[n] can be calculated as thedifference:S _(y) _(O2) _(,y) _(O2) (f,t)=S _(y) _(O) _(,y) _(O) (f,t)−S _(y) _(O1)_(,y) _(O1) (f,t)  (34)wherein S_(y) _(O) _(,y) _(O) (f,t) and S_(y) _(O1) _(,y) _(O1) (f,t)are the PSDs of the digital signals y_(O) [n] and y_(O1) [n],respectively, which can be approximated by known methods (e.g. Welch'smethod). By analyzing the time-dependent auto-covariance c_(y) _(O2)_(,y) _(O2) (t,u) of the second summand y_(O2)[n], and using theapproximation of equation 33 it can be shown that the PSD S_(φ,φ)(f) canbe expressed as:

$\begin{matrix}{{{S_{\varphi,\varphi}(f)} \approx \frac{4\left( {{S_{y_{O},y_{O}}\left( {f,t} \right)} - {S_{y_{O\; 1},y_{O\; 1}}\left( {f,t} \right)}} \right)}{\left( {A^{2}A_{O}} \right)^{2}\left( {1 - {\cos\left( {2\pi\; f\;\tau_{O}} \right)}} \right)\left( {1 - {\cos\left( {{4\pi\; f_{BO}t} + {2\Phi_{O}}} \right)}} \right)}},} & (35)\end{matrix}$wherein the nominator of the fraction is calculated in accordance withequation 34. Similar as mentioned above with regard to equation 32, itis noted that the resulting PSD S_(φ,φ)(f) of the phase noise φ(t) isevaluated over the whole chirp bandwidth B rather than at a fixedfrequency.

The present approach is summarized with reference of FIG. 11, which ismainly identical with FIG. 10 except that the function blocks 401, 402and 403 are replaced by function blocks 501 and 502. Function block 501represents the calculation of the PSD S_(y) _(O) _(,y) _(O) (f,t) andfunction block 502 the calculation of the desired PSD S_(φ,φ)(f) of thephase noise φ(t) in accordance with equations 34 and 35. The PSDS_(φ,φ)(f) can be provided to any internal or external controller, whichmay control the function of the FMCW radar dependent on the currentvalues of the PSD of the phase noise φ(t).

In accordance with a further exemplary embodiment the radar deviceincludes a noise cancellation function. Accordingly, the radar deviceincludes an RF transceiver configured to transmit an RF oscillatorsignal to a radar channel and receive a respective first RF radar signalfrom the radar channel, and an artificial radar target composed ofcircuitry that provides a gain and a delay to the RF oscillator signalto generate a second RF radar signal. A first frequency conversioncircuit includes a first mixer configured to down-convert the first RFradar signal; a second frequency conversion circuit includes a secondmixer configured to down-convert the second RF radar signal. Ananalog-to digital conversion unit is configured to digitize thedown-converted first RF radar signal and the down converted second RFradar signal to generate a first digital signal and a second digitalsignal, respectively. A digital signal processing unit receives thefirst and second digital signals and is configured to: calculate adecorrelated phase noise signal included in the second digital signal,to generate a cancellation signal based on the estimated decorrelatedphase noise signal, and to subtract the cancellation signal from thefirst digital radar signal to obtain a noise compensated digital radarsignal. Additionally, the digital signal processing unit is configuredto calculate a power spectral density of the decorrelated phase noisefrom the decorrelated phase noise signal, and to calculate the powerspectral density of the decorrelated phase noise into a power spectraldensity of the phase noise of an RF oscillator signal.

Moreover, a method for cancelling noise in a radar signal is described.IN accordance with one embodiment, the method comprises transmitting anRF oscillator signal to a radar channel and receiving a respective firstRF radar signal from the radar channel, applying the RF oscillatorsignal to an artificial radar target composed of circuitry, whichapplies a delay and a gain to the RF oscillator signal, to generate asecond RF radar signal. The method further comprises down-converting thefirst RF radar signal and the second RF radar signal from a RF frequencyband to a base band, digitizing the down-converted first RF radar signaland the down-converted second RF radar signal to generate a firstdigital signal and a second digital signal, respectively, andcalculating a decorrelated phase noise signal included in the seconddigital signal. A cancellation signal is generated based on thedecorrelated phase noise signal, and the cancellation signal issubtracted from the first digital radar signal to obtain a noisecompensated digital radar signal. Additionally, the method includescalculating a power spectral density of the decorrelated phase noisefrom the decorrelated phase noise signal, and converting the powerspectral density of the decorrelated phase noise into a power spectraldensity of the phase noise of an RF oscillator signal.

In a further embodiment, a radar device includes an RF transceiverconfigured to transmit an RF oscillator signal to a radar channel andreceive a respective first RF radar signal from the radar channel andfurther includes an artificial radar target composed of circuitry thatprovides a gain and a delay to the RF oscillator signal to generate asecond RF radar signal. A first frequency conversion circuit includes afirst mixer configured to down-convert the first RF radar signal, and asecond frequency conversion circuit includes a second mixer configuredto down-convert the second RF radar signal. An analog-to digitalconversion unit is configured to digitize the down-converted first RFradar signal and the down converted second RF radar signal to generate afirst digital signal and a second digital signal, respectively.Furthermore, a digital signal processing unit of the radar devicereceives the first and second digital signals and is configured tocalculate a decorrelated phase noise signal included in the seconddigital signal, to generate a cancellation signal based on the estimateddecorrelated phase noise signal, and to subtract the cancellation signalfrom the first digital radar signal to obtain a noise compensateddigital radar signal. Additionally, the digital signal processing unitis configured to calculate a power spectral density of the digital radarsignal, to calculate a power spectral density of a deterministic summandof the digital radar signal, and to calculate a power spectral densityof the phase noise of the RF oscillator signal based on the powerspectral density of the digital radar signal and the power spectraldensity of a deterministic summand.

Another exemplary method for cancelling noise in a radar signalcomprises transmitting an RF oscillator signal to a radar channel andreceiving a respective first RF radar signal from the radar channel, andapplying the RF oscillator signal to an artificial radar target composedof circuitry, which applies a delay and a gain to the RF oscillatorsignal, to generate a second RF radar signal. The first RF radar signaland the second RF radar signal are down-converted from a RF frequencyband to a base band, and the down-converted first RF radar signal andthe down-converted second RF radar signal are digitized to generate afirst digital signal and a second digital signal, respectively. Themethod further comprises calculating a decorrelated phase noise signalincluded in the second digital signal, generating a cancellation signalbased on the decorrelated phase noise signal, and subtracting thecancellation signal from the first digital radar signal to obtain anoise compensated digital radar signal. Additionally a power spectraldensity of the digital radar signal is calculated, a power spectraldensity of a deterministic summand of the digital radar signal iscalculated, and a power spectral density of the phase noise of the RFoscillator signal is then calculated based on the power spectral densityof the digital radar signal and the power spectral density of adeterministic summand.

Although the invention has been illustrated and described with respectto one or more implementations, alterations and/or modifications may bemade to the illustrated examples without departing from the spirit andscope of the appended claims. In particular regard to the variousfunctions performed by the above described components or structures(units, assemblies, devices, circuits, systems, etc.), the terms(including a reference to a “means”) used to describe such componentsare intended to correspond—unless otherwise indicated—to any componentor structure, which performs the specified function of the describedcomponent (e.g., that is functionally equivalent), even though notstructurally equivalent to the disclosed structure, which performs thefunction in the herein illustrated exemplary implementations of theinvention.

In addition, while a particular feature of the invention may have beendisclosed with respect to only one of several implementations, suchfeature may be combined with one or more other features of the otherimplementations as may be desired and advantageous for any given orparticular application. Furthermore, to the extent that the terms“including”, “includes”, “having”, “has”, “with”, or variants thereofare used in either the detailed description and the claims, such termsare intended to be inclusive in a manner similar to the term“comprising”.

What is claimed is:
 1. A method for estimating phase noise of radiofrequency (RF) oscillator signal in a frequency-modulatedcontinuous-wave (FMCW) radar system, the method comprising: applying theRF oscillator signal to an artificial radar target composed ofcircuitry, which applies a delay and a gain to the RF oscillator signal,to generate a RF radar signal; down-converting the RF radar signalreceived from the artificial radar target from a RF frequency band to abase band; digitizing the down-converted RF radar signal to generate adigital radar signal; calculating a decorrelated phase noise signal fromthe digital radar signal; calculating a power spectral density of thedecorrelated phase noise from the decorrelated phase noise signal; andconverting the power spectral density of the decorrelated phase noiseinto a power spectral density of the phase noise of the RF oscillatorsignal.
 2. The method of claim 1, wherein calculating the decorrelatedphase noise signal from the digital radar signal is done in accordancewith the equation:${{{\Delta\varphi}_{O}\lbrack n\rbrack} \approx \frac{{\frac{A_{O}}{2} \cdot {\cos\left( {{2\pi\; f_{BO}{nT}_{A}} + \Phi_{O}} \right)}} - \frac{y_{O}\lbrack n\rbrack}{A^{2}}}{\frac{A_{O}}{2} \cdot {\sin\left( {{2\pi\; f_{BO}{nT}_{A}} + \Phi_{O}} \right)}}},$wherein n is a time index, T_(A) is a sampling period, Δφ_(O) is thedecorrelated phase noise signal, y_(O)[n] is digital radar signal, A isthe amplitude of the RF oscillator signal, A_(O) is the gain of theartificial radar target, f_(BO) is a beat frequency and Φ_(O) is a phaseoffset.
 3. The method of claim 1, wherein converting power spectraldensity of the decorrelated phase noise into the power spectral densityof the phase noise of an RF oscillator signal is done in accordance withthe equation:${{S_{\varphi,\varphi}(f)} = \frac{S_{{\Delta\varphi}_{O},{\Delta\varphi}_{O}}(f)}{2\left( {1 - {\cos\left( {2\pi\; f\;\tau_{O}} \right)}} \right)}},$wherein S_(φ,φ)(f) is the power spectral density of the phase noise,S_(Δφ) _(O) _(,Δφ) _(O) (f) is the power spectral density of thedecorrelated phase noise and T_(O) is the delay of the artificial radartarget.
 4. The method of claim 1, wherein the RF oscillator signal issupplied to at least one antenna to be radiated as electromagnetic radarsignal.
 5. The method of claim 1, wherein the RF oscillator signal is asequence of chirps and signal parameters of the RF oscillator signalinclude a start frequency, a bandwidth, and a duration of the chirps. 6.The method of claim 1, wherein calculating the decorrelated phase noisesignal comprises: calculating an estimation of the decorrelated phasenoise signal dependent on the gain and the delay of the artificial radartarget and dependent on signal parameters of the RF oscillator signal.7. The method of claim 6, wherein the signal parameters of the RFoscillator signal include a start frequency, a bandwidth, and a durationof the chirps.
 8. The method of claim 1, wherein applying the RFoscillator signal to the artificial radar target comprises applying thedelay to the RF oscillator signal according to a back-scattering timerepresentative of a distance to a real target.
 9. The method of claim 8,wherein the back-scattering time is adjustable and applying the delay tothe RF oscillator signal comprises adjusting the delay according to theadjustable back-scattering time.
 10. The method of claim 8, wherein theback-scattering time is adjustable and applying the delay to the RFoscillator signal comprises adjusting the delay according to a pluralityof different back-scattering times representative of different distancesto real targets.
 11. The method of claim 1, wherein applying the RFoscillator signal to the artificial radar target comprises applying thegain to the RF oscillator signal according to a range representative ofa distance to a real target.
 12. The method of claim 11, wherein thegain is adjustable and applying the gain to the RF oscillator signalcomprises adjusting the gain according to a plurality of differentranges representative of different distances to real targets.
 13. Amethod for estimating phase noise of a radio frequency (RF) oscillatorsignal in a frequency-modulated continuous-wave (FMCW) radar system; themethod comprising: applying the RF oscillator signal to an artificialradar target composed of circuitry, which applies a delay and a gain tothe RF oscillator signal, to generate a RF radar signal; down-convertingthe RF radar signal received from the artificial radar target from a RFfrequency band to a base band; digitizing the down-converted RF radarsignal to generate a digital radar signal; calculating a power spectraldensity of the digital radar signal; calculating a power spectraldensity of a deterministic summand of the digital radar signal, whereinthe deterministic summand represents signal components due toreflections at at least one actual radar target; and calculating a powerspectral density of the phase noise of the RF oscillator signal based onthe power spectral density of the digital radar signal and the powerspectral density of the deterministic summand.
 14. The method of claim13, wherein calculating the power spectral density of the deterministicsummand of the digital radar signal comprises using Welch's method. 15.The method of claim 13, wherein calculating the power spectral densityof the phase noise of the RF oscillator signal comprises calculating adifference between the power spectral density of the digital radarsignal and the power spectral density of a deterministic summand toobtain a power spectral density of a stochastic summand of the digitalradar signal.
 16. The method of claim 15, further comprising convertingthe power spectral density of the deterministic summand into the powerspectral density of the phase noise of the RF oscillator signal.
 17. Themethod of claim 13, wherein applying the RF oscillator signal to theartificial radar target comprises applying the delay to the RFoscillator signal according to a back-scattering time representative ofa distance to a real target.
 18. The method of claim 17, whereinapplying the RF oscillator signal to the artificial radar targetcomprises applying the gain to the RF oscillator signal according to arange representative of the distance to the real target.
 19. A radardevice comprising: a local oscillator configured to generate a radiofrequency (RF) oscillator signal, which includes phase noise; anartificial radar target composed of circuitry, which is configured toapply a delay and a gain to the RF oscillator signal, to generate a RFradar signal; a first frequency conversion circuit configured todown-convert the RF radar signal received from the artificial radartarget from a RF frequency band to a base band; an analog-to digitalconversion unit configured to digitize the down-converted RF radarsignal to generate a digital radar signal; and a signal processing unitconfigured to: calculate a decorrelated phase noise signal from thedigital radar signal, calculate a power spectral density of thedecorrelated phase noise from the decorrelated phase noise signal, andconvert the power spectral density of the decorrelated phase noise intoa power spectral density of the phase noise of an RF oscillator signal.20. A radar device comprising: a local oscillator configured to generatea radio frequency (RF) oscillator signal, which includes phase noise; anartificial radar target composed of circuitry, which is configured toapply a delay and a gain to the RF oscillator signal, to generate a RFradar signal; a first frequency conversion circuit configured todown-convert the RF radar signal received from the artificial radartarget from a RF frequency band to a base band; an analog-to digitalconversion unit configured to digitize the down-converted RF radarsignal to generate a digital radar signal; and a signal processing unitconfigured to: calculate a power spectral density of the digital radarsignal, calculate a power spectral density of a deterministic summand ofthe digital radar signal, wherein the deterministic summand representssignal components due to reflections at at least one actual radartarget, and calculate a power spectral density of the phase noise of theRF oscillator signal based on the power spectral density of the digitalradar signal and the power spectral density of the deterministicsummand.